This proposal describes a five-year development plan for Rahul Deo to achieve independence as an investigator in the computational biology of cardiometabolic (CM) disease. Dr. Deo is a Cardiology Fellow at the Massachusetts General Hospital (MGH). The path described herein will enable him to build upon his background in molecular biophysics and complex disease genetics by taking advantage of the bioinformatics research and training opportunities at Harvard Medical School (HMS) and the clinical strengths of MGH. Dr. Deo will be co-mentored by Frederick 'Fritz' Roth, an associate professor in the Department of Biological Chemistry and Molecular Pharmacology at HMS and Robert Gerszten, an associate professor in the Department of Medicine at Harvard Medical School, and Director of the Metabolomics Platform at the Broad Institute of Harvard and MIT. Dr. Roth is a recognized expert in the computational biology of large omic data sets while Dr. Gerszten is an expert in metabolomics, with particular application to CM disease. In addition to having worked closely together over the past five years on numerous metabolomics projects, Drs. Roth and Gerszten each have a strong record of mentorship. Dr. Deo will also work closely with Drs. Marc Vidal, Joseph Loscalzo, Isaac Kohane and Calum MacRae, who will provide career guidance and scientific advice on the execution of the proposed research plan. The research program will emphasize the use of bioinformatics techniques and metabolite profiling to advance the characterization and classification of CM disease. There is increasing recognition that our current disease categorization approaches are inadequate to describe the scope and heterogeneity of human disease. Metabolomics - the analysis of metabolite levels from biologic fluid samples - is one non-invasive way to obtain quantitative molecular phenotypes from patients to address this complexity. This research plan is designed to assess the hypothesis that the application of modern computational methods, previously developed for large high-throughput biological omic data, to the analysis of metabolite profiling data will help us improve disease elucidation. Specifically, this program proposes: 1) to use data integration and network approaches to characterize biologic responses to cardiometabolic (CM) perturbations and 2) to use related bioinformatic analytic techniques to build and test metabolite classifiers distinguishing CM disease patients from controls